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Low-LOD code-driven identification of the high seismic risk areas for industrial buildings in Italy

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Abstract

The identification of the areas with high seismic risk for industrial buildings is of paramount importance for decision-makers to implement risk reduction policies. In this context, this study aims at performing a low-LOD (Level of Detail) code-driven identification of areas in Italy where industrial buildings face high seismic risk. Firstly, the hazard, vulnerability, and exposure models are introduced. Then, such input data are validated by comparing the predictions of the proposed framework with the observational data obtained after the Emilia-Romagna earthquakes of 2012. The validated framework is used to predict the conditional (to a certain return period) and unconditional risk in terms of elements-at-risk and economic losses. The unconditional risk in terms of economic losses expressed in terms of Expected Annual Losses (EAL) is used to rank areas in Italy in terms of seismic risk for industrial buildings. As expected, the area hit by the Emilia-Romagna earthquakes in 2012 is included among those with the highest seismic risk, indicating the predictability of large damage occurred on industrial buildings during the seismic event. Finally, some conclusions are drawn about the implications for future policies and studies for seismic risk reduction of industrial buildings in Italy.

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Data availability

The data that support the findings of this study are available from the corresponding author, C.D., upon reasonable request.

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Acknowledgements

DPC-ReLUIS 2019-2021 project is acknowledged for the financial support given to the present research. Authors are grateful to “Agenzia delle Entrate” (Italian Tax Revenue Agency) for providing data analyzed in this study. The first author is acknowledging the Zhejiang University–University of Illinois at Urbana Champaign Institute (ZJUI) for the financial support given to the present research.

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Authors and Affiliations

  1. Zhejiang University - University of Illinois at Urbana Champaign Institute, Zhejiang University, 718 East Haizhou Road, Haining, 314400, Zhejiang, People’s Republic of China

    Cristoforo Demartino

  2. College of Civil Engineering and Architecture, Zhejiang University, 866 Yuhangtang Road, Hangzhou, 310058, Zhejiang, People’s Republic of China

    Giorgio Monti

  3. Department of Structural Engineering and Geotechnics, Sapienza University of Rome, Via A. Gramsci 53, 00197, Rome, Italy

    Giorgio Monti

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  1. Cristoforo Demartino
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Correspondence to Cristoforo Demartino.

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Appendix A: Exposure models not employed

Appendix A: Exposure models not employed

1.1 A1 Industrial districts

Starting from the Local Labor Systems (Sistemi Locali del Lavoro—SLL), on the basis of the analysis of their production specialization, ISTAT (Italian National Institute of Statistics) has identified 141 industrial districts using the data of the economic units surveyed in 2011 through the 9th General Census of industry and services. Italian industrial district in 2011 from ISTAT are depicted in Fig. 18.

Fig. 18
figure 18

Italian industrial district in 2011 from ISTAT (Italian National Institute of Statistics)

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1.2 A2 Corine land cover

The CORINE Land Cover (CLC) is a vector map with a scale of 1:100 000, a minimum cartographic unit of 0.25 km2. CLC database is based on the visual interpretation that is a method of recognizing, identifying and assessing of objects recorded in areal or satellite images. CLC database contains a computerized inventory on land cover of the 27 EC member states and other European countries using 44 classes of the 3-level Corine nomenclature. Industrial buildings are considered located in areas with CLC class level 1.2 (Artificial areas—Industrial, commercial and transport units) that is defined as “Areas mainly occupied by industrial activities of manufacturing, trade, financial activities and services, transport infrastructures for road traffic and rail networks, airport installations, river and sea port installations, including their associated lands and access infrastructures. Includes industrial livestock rearing facilities”. The areas where the industrial buildings are located according to CLC are reported in Fig. 19.

Fig. 19
figure 19

Industrial or commercial units from CLC (CORINE Land Cover)

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1.3 A3 OpenStreet Map

OpenStreetMap (OSM) is a collaborative project to create a free editable map of the world and is probably the best known example of volunteered geographic information. OSM represents physical features on the ground (e.g., roads or buildings) using tags attached to its basic data structures (its nodes, ways, and relations). Each tag describes a geographic attribute of the feature being shown by that specific node, way or relation. In particular, the industrial buildings are categorized as with the key “building” and the value “industrial” that is defined as “A building where some industrial process takes place”. Use warehouse if the purpose is known to be primarily for storage/distribution”. The so-derived distribution of the industrial buildings according to OSM is reported in Fig. 20.

Fig. 20
figure 20

Industrial buildings from OSM (OpenStreetMap)

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Demartino, C., Monti, G. Low-LOD code-driven identification of the high seismic risk areas for industrial buildings in Italy. Bull Earthquake Eng 18, 4421–4452 (2020). https://doi.org/10.1007/s10518-020-00867-3

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